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Towards a Business Case for Vehicle-To-Grid—Maximizing Profits

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Towards a Business Case for Vehicle-To-Grid—Maximizing Profits Chapter 8 Towards a Business Case for Vehicle-to-Grid—Maximizing Profits in Ancillary Service Markets David Ciechanowicz, Alois Knoll, Patrick Osswald and Dominik Pelzer Abstract Employing plug-in electric vehicles (PEV) as energy buffers in a smart grid could contribute to improved power grid stability and facilitate the integration of renewable energies. While the technical feasibility of this concept termed vehicle- to-grid (V2G) has been extensively demonstrated, economic concerns remain a crucial barrier for its implementation into practice. A common drawback of previous economic viability assessments, however, is their static approach based on average values which neglects intrinsic system dynamics. Realistically assessing the eco- nomics of V2G requires modeling an intelligent agent as a homo economicus who exploits all available information with regard to maximizing its utility. Therefore, a smart control strategy built on real-time information, prediction and more sophis- ticated battery models is proposed in order to optimize an agent’s market partici- pation strategy. By exploiting this information and by dynamically adapting the agent behavior at each time step, an optimal control strategy for energy dispatches of each single PEV is derived. The introduced cost-revenue model, the battery model, and the optimization model are applied in a case study building on data for Singa- pore. It is the aim of this work to provide a comprehensive view on the economic aspects of V2G which are essential for making it a viable business case. Keywords Vehicle-to-grid Á Plug-in electric vehicle Á Economic viability Á Ancillary services Á Battery modeling Á Optimization model D. Ciechanowicz (&) Á P. Osswald Á D. Pelzer TUM CREATE, Singapore, Singapore e-mail: [email protected] P. Osswald e-mail: [email protected] D. Pelzer e-mail: [email protected] A. Knoll Institute for Informatics VI, Robotics and Embedded Systems, Technische Universität München (TUM), Munich, Germany e-mail: [email protected] © Springer Science+Business Media Singapore 2015 203 S. Rajakaruna et al. (eds.), Plug In Electric Vehicles in Smart Grids, Power Systems, DOI 10.1007/978-981-287-302-6_8 204 D. Ciechanowicz et al. 8.1 Introduction In power systems, fluctuations of energy demand and supply cause continuous deviations from the desired frequency. Ensuring power grid stability requires an instantaneous response by the power system operator which restores the equilib- rium between demand and supply. This is either achieved by power plants capable of quickly adjusting their power output or by storage facilities which buffer energy excesses or shortages. Most of these solutions are, however, either costly, entail large space or exhibit low energy efficiencies leading to the need for development of alternative approaches. One possible solution could be the utilization of plug-in electric vehicles (PEV) of which batteries could be employed as short term energy storage through charging in the case of a power excess or by feeding electricity back to the grid in the opposite case. This concept termed vehicle-to-grid (V2G) was first mentioned in 1997 [1] and has been subject to intensive research in the last two decades. While the effectiveness of the V2G concept to improve power grid stability has been confirmed by both theoretical considerations [2–8] as well as fully functional prototypes [5, 7, 9], its economic viability is still subject to controversial discus- sions. This is reflected in the diverging conclusions on the profitability where some expect annual losses of several thousand dollars while others promise multiple thousand dollars of yearly income [2, 6, 9–15]. One drawback of previous economic analyses of the V2G concept is that cal- culations are based on average annual values for the involved parameters. In reality, however, electricity prices highly vary during the course of a day, presenting varying scenarios where V2G may yield profits in one time period but result in losses in a different one. Furthermore, individual travel itineraries impose restric- tions on the temporal availability of PEVs. At the same time, factors such as battery aging typically depend non-linearly on a variety of parameters which cannot be kept constant during V2G operation. Simple averaging therefore does not yield correct cost estimations. The entity of these aspects significantly limits the explanatory power of static approaches and leaves the outcome of these methods highly sen- sitive to the choice of the input parameters. To correctly determine the economic viability of V2G and at the same time provide a control strategy for individual V2G agents, more dynamic approaches are required. The purpose of this chapter is to discuss the problems of previous approaches investigating the economic viability of V2G and to identify solutions that could pave the way for making V2G an economically viable business case. The remainder of this work is structured as follows: In Sect. 8.2, the transition from a power system to a smart grid is described. In this context, the V2G concept is discussed as one possible future solution for improving power grid stability. Section 8.3 intro- duces an electricity market independent V2G control strategy which aims for maximizing profits in ancillary service markets. This concept includes an appro- priate consideration of battery depreciation as well as an optimization methodology. The optimization model is then applied in a simple case study building on data for 8 Towards a Business Case for Vehicle-to-Grid … 205 Singapore in Sect. 8.4, followed by a discussion of parameter sensitivities. In Sects. 8.5 and 8.6 findings are finally discussed and an outlook on future research is given. 8.2 Power System Fundamentals In the first part of this section, the fundamentals of the power system and the concept of ancillary services are briefly introduced. It is discussed, how the tran- sition to a smart grid could mitigate the increasing need for balancing power demand and supply which arises from the growing share of renewable energy sources. This leads to the possible role of PEVs and the V2G concept in the future power grid, which is described in more detail in Sect. 8.2.2. One important com- ponent for the implementation of V2G is the aggregator which is finally discussed in Sect. 8.2.3. 8.2.1 Power System and Smart Grid A power system is a network of power lines which connect energy producing and consuming entities with each other. Different voltage levels may distinguish the power grid into a maximum, high, medium, and low voltage grid with the first two levels forming the transmission grid and the latter two the distribution grid. The different levels are physically separated from each other by substations, switches, and transformers and are controlled by high performance computers. In Fig. 8.1 a rough illustration of the Singapore power system as it can be derived from data on high-voltage grid, substations and consumers is exemplarily shown. The upper layer shows the transmission grid while the middle layer depicts Fig. 8.1 Singapore power system 206 D. Ciechanowicz et al. the distribution grid. The nodes in both layers represent power plants, substations, switches or transformers which in this case are not visually distinguished. The nodes at the bottom layer on the map depict a selection of consumers connected to the distribution grid. A power grid is operated by one or more transmission system operators (TSO) of which its primary task is the energy transfer from the generation units to regional or local distribution system operators (DSO) which then deliver the energy to the consumers. One key responsibility of a TSO is to ensure power grid stability. The power grid itself only exhibits negligible energy storage capacity and is therefore an inherently unstable system. Deviations between energy demand and supply which may either be positive when supply exceeds demand or negative in the opposite case therefore lead to fluctuations of voltage and frequency which require an immediate action. This response is performed by so called ancillary services provided by fast responding power generators which are capable of quickly ramping up or curbing down their power output. Depending on the response time and the duration of providing ancillary services, it is distinguished between regulation as well as pri- mary, secondary, and contingency reserve. All of the four markets usually have a ratio of around 1 % of the total annual energy generation. Providers of ancillary services receive a payment for the dispatched energy when up-regulation is required or a compensation for curbing power generation in the opposite case. These energy payments are usually differentiated and considerably higher for regulation than for reserve. Besides these energy payments, many national electricity markets also have a capacity payment which is a reward solely for holding power generation potential available instead of energy dispatch. In most markets, prices are fairly variable over time but are kept constant for a certain time period of 15 or 30 mins in most cases. As a result of growing shares of intermittent renewable energy sources and the introduction of PEVs on a large scale, the need for ancillary services and energy storage is increasing. This is because both the availability of renewable energies and the mobility pattern of PEVs are volatile and sometimes hard to predict. To satisfy the additional demand for ancillary services, either fast reacting generators or energy storage facilities are needed. Technologies capable of providing this func- tionality include pumped storage hydroelectricity (PSH), compressed air energy storage (CAES), hydrogen-driven fuel-cells,orsupercapacitors. These technolo- gies are, however, often costly, energy inefficient or may entail large space leading to the need for alternative approaches. The need for energy storage may be reduced in a smart grid which supports multi-directional energy flow instead of showing a strictly hierarchical topology.
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